[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M70H7SMDDSX01M5WKNCFDEV2":3,"skill-same:01M70H7SMDDSX01M5WKNCFDEV2":73},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M70H7SMDDSX01M5WKNCFDEV2","social-graph-ranker",null,"Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.","mdnaimul22\u002Fhuman-skills","mdnaimul22","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F64520358?v=4","01M70FH95MH2H796Q01TYSFZ03","Naimul","01M70FH96798CQR4YHA13F17GA",3,"MIT","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Faffaan-m_ECC\u002Fsocial-graph-ranker\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"graph","social graph ranker","social","when","workflow","for","top","ranking","gap analysis","across","scoring","reusable","use","bridge","linkedin","intro","engine","discovery","analysis","network","2026-11-10 10:12:30",false,"---\nname: social-graph-ranker\ndescription: Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.\nmetadata:\n  origin: ECC\n---\n\n# Social Graph Ranker\n\nCanonical weighted graph-ranking layer for network-aware outreach.\n\nUse this when the user needs to:\n\n- rank existing mutuals or connections by intro value\n- map warm paths to a target list\n- measure bridge value across first- and second-order connections\n- decide which targets deserve warm intros versus direct cold outreach\n- understand the graph math independently from `lead-intelligence` or `connections-optimizer`\n\n## When To Use This Standalone\n\nChoose this skill when the user primarily wants the ranking engine:\n\n- \"who in my network is best positioned to introduce me?\"\n- \"rank my mutuals by who can get me to these people\"\n- \"map my graph against this ICP\"\n- \"show me the bridge math\"\n\nDo not use this by itself when the user really wants:\n\n- full lead generation and outbound sequencing -> use `lead-intelligence`\n- pruning, rebalancing, and growing the network -> use `connections-optimizer`\n\n## Inputs\n\nCollect or infer:\n\n- target people, companies, or ICP definition\n- the user's current graph on X, LinkedIn, or both\n- weighting priorities such as role, industry, geography, and responsiveness\n- traversal depth and decay tolerance\n\n## Core Model\n\nGiven:\n\n- `T` = weighted target set\n- `M` = your current mutuals \u002F direct connections\n- `d(m, t)` = shortest hop distance from mutual `m` to target `t`\n- `w(t)` = target weight from signal scoring\n\nBase bridge score:\n\n```text\nB(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)\n```\n\nWhere:\n\n- `λ` is the decay factor, usually `0.5`\n- a direct path contributes full value\n- each extra hop halves the contribution\n\nSecond-order expansion:\n\n```text\nB_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\\\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))\n```\n\nWhere:\n\n- `N(m) \\\\ M` is the set of people the mutual knows that you do not\n- `α` discounts second-order reach, usually `0.3`\n\nResponse-adjusted final ranking:\n\n```text\nR(m) = B_ext(m) · (1 + β · engagement(m))\n```\n\nWhere:\n\n- `engagement(m)` is normalized responsiveness or relationship strength\n- `β` is the engagement bonus, usually `0.2`\n\nInterpretation:\n\n- Tier 1: high `R(m)` and direct bridge paths -> warm intro asks\n- Tier 2: medium `R(m)` and one-hop bridge paths -> conditional intro asks\n- Tier 3: low `R(m)` or no viable bridge -> direct outreach or follow-gap fill\n\n## Scoring Signals\n\nWeight targets before graph traversal with whatever matters for the current priority set:\n\n- role or title alignment\n- company or industry fit\n- current activity and recency\n- geographic relevance\n- influence or reach\n- likelihood of response\n\nWeight mutuals after traversal with:\n\n- number of weighted paths into the target set\n- directness of those paths\n- responsiveness or prior interaction history\n- contextual fit for making the intro\n\n## Workflow\n\n1. Build the weighted target set.\n2. Pull the user's graph from X, LinkedIn, or both.\n3. Compute direct bridge scores.\n4. Expand second-order candidates for the highest-value mutuals.\n5. Rank by `R(m)`.\n6. Return:\n   - best warm intro asks\n   - conditional bridge paths\n   - graph gaps where no warm path exists\n\n## Output Shape\n\n```text\nSOCIAL GRAPH RANKING\n====================\n\nPriority Set:\nPlatforms:\nDecay Model:\n\nTop Bridges\n- mutual \u002F connection\n  base_score:\n  extended_score:\n  best_targets:\n  path_summary:\n  recommended_action:\n\nConditional Paths\n- mutual \u002F connection\n  reason:\n  extra hop cost:\n\nNo Warm Path\n- target\n  recommendation: direct outreach \u002F fill graph gap\n```\n\n## Related Skills\n\n- `lead-intelligence` uses this ranking model inside the broader target-discovery and outreach pipeline\n- `connections-optimizer` uses the same bridge logic when deciding who to keep, prune, or add\n- `brand-voice` should run before drafting any intro request or direct outreach\n- `x-api` provides X graph access and optional execution paths\n","# Social Graph Ranker\n\nCanonical weighted graph-ranking layer for network-aware outreach.\n\nUse this when the user needs to:\n\n- rank existing mutuals or connections by intro value\n- map warm paths to a target list\n- measure bridge value across first- and second-order connections\n- decide which targets deserve warm intros versus direct cold outreach\n- understand the graph math independently from `lead-intelligence` or `connections-optimizer`\n\n## When To Use This Standalone\n\nChoose this skill when the user primarily wants the ranking engine:\n\n- \"who in my network is best positioned to introduce me?\"\n- \"rank my mutuals by who can get me to these people\"\n- \"map my graph against this ICP\"\n- \"show me the bridge math\"\n\nDo not use this by itself when the user really wants:\n\n- full lead generation and outbound sequencing -> use `lead-intelligence`\n- pruning, rebalancing, and growing the network -> use `connections-optimizer`\n\n## Inputs\n\nCollect or infer:\n\n- target people, companies, or ICP definition\n- the user's current graph on X, LinkedIn, or both\n- weighting priorities such as role, industry, geography, and responsiveness\n- traversal depth and decay tolerance\n\n## Core Model\n\nGiven:\n\n- `T` = weighted target set\n- `M` = your current mutuals \u002F direct connections\n- `d(m, t)` = shortest hop distance from mutual `m` to target `t`\n- `w(t)` = target weight from signal scoring\n\nBase bridge score:\n\n```text\nB(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)\n```\n\nWhere:\n\n- `λ` is the decay factor, usually `0.5`\n- a direct path contributes full value\n- each extra hop halves the contribution\n\nSecond-order expansion:\n\n```text\nB_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\\\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))\n```\n\nWhere:\n\n- `N(m) \\\\ M` is the set of people the mutual knows that you do not\n- `α` discounts second-order reach, usually `0.3`\n\nResponse-adjusted final ranking:\n\n```text\nR(m) = B_ext(m) · (1 + β · engagement(m))\n```\n\nWhere:\n\n- `engagement(m)` is normalized responsiveness or relationship strength\n- `β` is the engagement bonus, usually `0.2`\n\nInterpretation:\n\n- Tier 1: high `R(m)` and direct bridge paths -> warm intro asks\n- Tier 2: medium `R(m)` and one-hop bridge paths -> conditional intro asks\n- Tier 3: low `R(m)` or no viable bridge -> direct outreach or follow-gap fill\n\n## Scoring Signals\n\nWeight targets before graph traversal with whatever matters for the current priority set:\n\n- role or title alignment\n- company or industry fit\n- current activity and recency\n- geographic relevance\n- influence or reach\n- likelihood of response\n\nWeight mutuals after traversal with:\n\n- number of weighted paths into the target set\n- directness of those paths\n- responsiveness or prior interaction history\n- contextual fit for making the intro\n\n## Workflow\n\n1. Build the weighted target set.\n2. Pull the user's graph from X, LinkedIn, or both.\n3. Compute direct bridge scores.\n4. Expand second-order candidates for the highest-value mutuals.\n5. Rank by `R(m)`.\n6. Return:\n   - best warm intro asks\n   - conditional bridge paths\n   - graph gaps where no warm path exists\n\n## Output Shape\n\n```text\nSOCIAL GRAPH RANKING\n====================\n\nPriority Set:\nPlatforms:\nDecay Model:\n\nTop Bridges\n- mutual \u002F connection\n  base_score:\n  extended_score:\n  best_targets:\n  path_summary:\n  recommended_action:\n\nConditional Paths\n- mutual \u002F connection\n  reason:\n  extra hop cost:\n\nNo Warm Path\n- target\n  recommendation: direct outreach \u002F fill graph gap\n```\n\n## Related Skills\n\n- `lead-intelligence` uses this ranking model inside the broader target-discovery and outreach pipeline\n- `connections-optimizer` uses the same bridge logic when deciding who to keep, prune, or add\n- `brand-voice` should run before drafting any intro request or direct outreach\n- `x-api` provides X graph access and optional execution paths\n",{"name":5,"metadata":49,"description":7},{"origin":50},"ECC","main","74715deacdc69119d739f38916f8ee4f0f979ebf","skills\u002Fstorage\u002Faffaan-m_ECC\u002Fsocial-graph-ranker",0,"https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills","https:\u002F\u002Fgithub.com\u002Fmdnaimul22","AI Agents Workflows Automation | Specializing in Python, FastAPI, MCP Agents & Custom ERP.","2026-11-11 21:16:57",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add mdnaimul22\u002Fhuman-skills --skill social-graph-ranker",{"tool":65,"command":66},"bunx","bunx skills add mdnaimul22\u002Fhuman-skills --skill social-graph-ranker",{"tool":68,"command":69},"pnpm","pnpm dlx skills add mdnaimul22\u002Fhuman-skills --skill social-graph-ranker","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Faffaan-m_ECC\u002Fsocial-graph-ranker\u002FSKILL.md",true,[],[74,82,108,129,148,163],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":81},"01M72JT3YP1SQHD0MANGDXQ4RT","higgsfield-websites","Build, edit, and deploy full-stack websites, apps and games via the Higgsfield CLI (`higgsfield website …`). Each is a React 19 + TanStack Start SSR app in one Cloudflare Worker (D1\u002FR2\u002FKV\u002FDO\u002FContainers). THREE product types, picked via `--type` on create: `website` (standalone, no Higgsfield integration — references\u002Fwebsite-flow.md), `app` (Sign in with Higgsfield + fnf SDK, Quanta — references\u002Fapp-flow.md), `game` (realtime multiplayer rooms — references\u002Fgame-flow.md). Routes to the right flow; each carries its own rules and deploy\u002Fpublish gates.\nUse when: \"build me a website\", \"make a landing page\", \"create a web app\", \"build a SaaS dashboard \u002F portfolio\", \"make me a game\", \"deploy this site\", \"publish\". Also owns GAME ART: \"make a spritesheet\", \"tileable texture\", \"animate a 3D character\", game music\u002FSFX — see the game-* references.\nNOT for: single image\u002Fvideo\u002Faudio generation (higgsfield-generate), product photos (higgsfield-product-photoshoot), marketplace cards (higgsfield-marketplace-cards).","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fhiggsfield-ai_skills\u002Fhiggsfield-websites\u002FSKILL.md",[],[],"2026-11-10 22:39:42",{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":100,"publishedAt":44},"01M70JPK8HAXXA85P7Q8NKZZYG","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fwriting-skills\u002FSKILL.md",[88,91,96,97],{"categoryNo":89,"categoryName":90,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":98,"categoryName":99,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[101,102,103,104,36,105,106,27,107],"writing","writing skills","editing","deployment","Existing","new","work",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":119,"publishedAt":44},"01M70JP4PHBH1CQVVDWZ94W159","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fwriting-plans\u002FSKILL.md",[114,115,116],{"categoryNo":89,"categoryName":90,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":92,"categoryName":93,"taxonomyType":94,"taxonomyTypeDesc":95},{"categoryNo":117,"categoryName":118,"taxonomyType":94,"taxonomyTypeDesc":95},"01JBSEEDCAT000000000000030","开发工程师",[101,120,121,122,27,123,29,36,124,125,126,127,128],"writing plans","plans","you","code","requirements","task","multi","spec","multi-step task",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":44},"01M70JNTYVGXFZT8PASZ844TH8","verification-before-completion","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fverification-before-completion\u002FSKILL.md",[],[136,137,138,139,140,141,36,142,143,144,27,145,107,146,147],"verification","verification before completion","complete","about","output","assertions","prs","evidence","claim","claims","commands","running",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":44},"01M70JNGVSQDESWN0Q5BPJ875V","using-superpowers","Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fusing-superpowers\u002FSKILL.md",[154],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[156,157,158,159,36,27,160,161,162],"using superpowers","superpowers","conversation","how to","how","find","questions",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":169,"publishedAt":44},"01M70JN57CE588NPYPYTWH1DWG","using-git-worktrees","Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback","https:\u002F\u002Fgithub.com\u002Fmdnaimul22\u002Fhuman-skills\u002Fblob\u002Fmain\u002Fskills\u002Fstorage\u002Fobra_superpowers\u002Fusing-git-worktrees\u002FSKILL.md",[],[170,171,172,173,174,175,27,107,176,177,178,179,180,181,182,121,36,183,184],"using git worktrees","Using git","git","worktrees","git worktrees","implementation","git worktree","worktree","native","Tools","isolation","fallback","feature","via","workspace"]